IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v59y2008i4d10.1057_palgrave.jors.2602423.html
   My bibliography  Save this article

Integration of production planning and scheduling using an expert system and a genetic algorithm

Author

Listed:
  • A Ławrynowicz

    (University of Technology and Life Sciences)

Abstract

In the traditional approaches, processes of planning and scheduling are done sequentially, where the process plan is determined before the actual scheduling is performed. This simple approach ignores the relationship between the scheduling and planning. Practical scheduling systems need to be able to react to significant real-time events within an acceptable response time and revise schedules appropriately. Therefore, the author proposes a new methodology with artificial intelligence to support production planning and scheduling in supply net. In this approach, the production planning problem is first solved, and then the scheduling problem is considered with the constraint of the solution. The approach is implemented as a combination of expert system and genetic algorithm. The research indicates that the new system yields better results in real-life supply net than using a traditional method. The results of experiments provide that the proposed genetic algorithm produces schedules with makespan that is average 21% better than the methods based on dispatching rules.

Suggested Citation

  • A Ławrynowicz, 2008. "Integration of production planning and scheduling using an expert system and a genetic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 455-463, April.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:4:d:10.1057_palgrave.jors.2602423
    DOI: 10.1057/palgrave.jors.2602423
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2602423
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2602423?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blazewicz, Jacek & Domschke, Wolfgang & Pesch, Erwin, 1996. "The job shop scheduling problem: Conventional and new solution techniques," European Journal of Operational Research, Elsevier, vol. 93(1), pages 1-33, August.
    2. Cowling, Peter & Johansson, Marcus, 2002. "Using real time information for effective dynamic scheduling," European Journal of Operational Research, Elsevier, vol. 139(2), pages 230-244, June.
    3. Koh, S.C. Lenny & Saad, Sameh M., 2006. "Managing uncertainty in ERP-controlled manufacturing environments in SMEs," International Journal of Production Economics, Elsevier, vol. 101(1), pages 109-127, May.
    4. Akkan, Can & Karabati, Selcuk, 2004. "The two-machine flowshop total completion time problem: Improved lower bounds and a branch-and-bound algorithm," European Journal of Operational Research, Elsevier, vol. 159(2), pages 420-429, December.
    5. Della Croce, F. & Ghirardi, M. & Tadei, R., 2002. "An improved branch-and-bound algorithm for the two machine total completion time flow shop problem," European Journal of Operational Research, Elsevier, vol. 139(2), pages 293-301, June.
    6. Percy, David F. & Kobbacy, Khairy A. H., 2000. "Determining economical maintenance intervals," International Journal of Production Economics, Elsevier, vol. 67(1), pages 87-94, August.
    7. J M Framinan & J N D Gupta & R Leisten, 2004. "A review and classification of heuristics for permutation flow-shop scheduling with makespan objective," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1243-1255, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    2. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    3. Sicheng Zhang & T.N. Wong, 2016. "Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4815-4838, August.
    4. Ławrynowicz Anna, 2011. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems," Foundations of Management, Sciendo, vol. 3(2), pages 7-26, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anna Ławrynowicz, 2006. "Hybrid approach with an expert system and a genetic algorithm to production management in the supply net," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(1‐2), pages 59-76, January.
    2. S Yanai & T Fujie, 2006. "A three-machine permutation flow-shop problem with minimum makespan on the second machine," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 460-468, April.
    3. Giuseppe Lancia & Franca Rinaldi & Paolo Serafini, 2011. "A time-indexed LP-based approach for min-sum job-shop problems," Annals of Operations Research, Springer, vol. 186(1), pages 175-198, June.
    4. Detienne, Boris & Sadykov, Ruslan & Tanaka, Shunji, 2016. "The two-machine flowshop total completion time problem: Branch-and-bound algorithms based on network-flow formulation," European Journal of Operational Research, Elsevier, vol. 252(3), pages 750-760.
    5. Federico Della Croce & Andrea Grosso & Fabio Salassa, 2014. "A matheuristic approach for the two-machine total completion time flow shop problem," Annals of Operations Research, Springer, vol. 213(1), pages 67-78, February.
    6. Mohamed Ali Rakrouki & Anis Kooli & Sabrine Chalghoumi & Talel Ladhari, 2020. "A branch-and-bound algorithm for the two-machine total completion time flowshop problem subject to release dates," Operational Research, Springer, vol. 20(1), pages 21-35, March.
    7. Ladhari, Talel & Rakrouki, Mohamed Ali, 2009. "Heuristics and lower bounds for minimizing the total completion time in a two-machine flowshop," International Journal of Production Economics, Elsevier, vol. 122(2), pages 678-691, December.
    8. Gharbi, Anis & Ladhari, Talel & Msakni, Mohamed Kais & Serairi, Mehdi, 2013. "The two-machine flowshop scheduling problem with sequence-independent setup times: New lower bounding strategies," European Journal of Operational Research, Elsevier, vol. 231(1), pages 69-78.
    9. Fernandez-Viagas, Victor & Molina-Pariente, Jose M. & Framinan, Jose M., 2020. "Generalised accelerations for insertion-based heuristics in permutation flowshop scheduling," European Journal of Operational Research, Elsevier, vol. 282(3), pages 858-872.
    10. Rossi, Andrea, 2014. "Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships," International Journal of Production Economics, Elsevier, vol. 153(C), pages 253-267.
    11. Barry B. & Quim Castellà & Angel A. & Helena Ramalhinho Lourenco & Manuel Mateo, 2012. "ILS-ESP: An Efficient, Simple, and Parameter-Free Algorithm for Solving the Permutation Flow-Shop Problem," Working Papers 636, Barcelona School of Economics.
    12. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    13. Wang, Ling & Sun, Lin-Yan & Sun, Lin-Hui & Wang, Ji-Bo, 2010. "On three-machine flow shop scheduling with deteriorating jobs," International Journal of Production Economics, Elsevier, vol. 125(1), pages 185-189, May.
    14. Gupta, Jatinder N.D. & Koulamas, Christos & Kyparisis, George J., 2006. "Performance guarantees for flowshop heuristics to minimize makespan," European Journal of Operational Research, Elsevier, vol. 169(3), pages 865-872, March.
    15. Jacomine Grobler & Andries Engelbrecht & Schalk Kok & Sarma Yadavalli, 2010. "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, Springer, vol. 180(1), pages 165-196, November.
    16. Jiewu Leng & Pingyu Jiang, 2019. "Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 979-994, March.
    17. Gerardo Minella & Rubén Ruiz & Michele Ciavotta, 2008. "A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 451-471, August.
    18. Pempera, Jaroslaw & Smutnicki, Czeslaw, 2018. "Open shop cyclic scheduling," European Journal of Operational Research, Elsevier, vol. 269(2), pages 773-781.
    19. B-J Joo & Y-D Kim, 2009. "A branch-and-bound algorithm for a two-machine flowshop scheduling problem with limited waiting time constraints," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(4), pages 572-582, April.
    20. Sivadasan, Suja & Smart, Janet & Huaccho Huatuco, Luisa & Calinescu, Anisoara, 2013. "Reducing schedule instability by identifying and omitting complexity-adding information flows at the supplier–customer interface," International Journal of Production Economics, Elsevier, vol. 145(1), pages 253-262.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorsoc:v:59:y:2008:i:4:d:10.1057_palgrave.jors.2602423. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.